This document is intended to give a demonstration–though not full tutorial or demo of features–of how Leaflet can be used in R to easily generate attractive, content-rich, interactive maps.
Although Leaflet is a Javascript package, folks in R’s open source community have created binding that allow for a great deal of Leaflet’s functionality to be available to R users, which decreases the training and logistical challenges to working across platforms when seeking to visualize and communicate our other data work.
While I have strived to create a helpful sequence for learning about Leaflet’s functionality in R, a more extensive tutorial on the topic can be found here.
<b>Some shit</b>Creating an interactive map with colors, markers, zoom, etc is as easy as just a few lines of code.
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=-87.5947675, lat=41.7858686, popup="Chapin Hall")
mBy default, Leaflet in R uses map tiles from Open Street Maps. However, it can easily access and use map tiles from other sources and styles.
m %>% addProviderTiles("CartoDB.Positron")It is easy to display data that has geographic components. The code below sources data on CPS school High School report cards from the City of Chicago Data Portal.
# Download and prep CPS report card data
schools <- read.csv("https://data.cityofchicago.org/api/views/2m8w-izji/rows.csv",
stringsAsFactors = FALSE) %>%
within({
temp <- Latitude
Latitude <- Longitude
Longitude <- temp
PolicyLevel <- factor(CPS.Performance.Policy.Level)
rm(temp)
}) # ...because these are accidentally backwards
# Download and merge an Excel file with enrollment data
download.file(url = "http://cps.edu/Performance/Documents/Datafiles/enrollment_20th_day_2014.xls",
destfile = "sch_enrollment_xls.xls", method = "curl")
sch_enrollment <- read_excel("sch_enrollment_xls.xls")
sch_merge <- merge(x = schools,
y = sch_enrollment,
by.x = "School.ID",
by.y = "School ID")
# Generate the map
leaflet(sch_merge) %>% addTiles() %>%
addMarkers(lng = ~Longitude, lat = ~Latitude, popup = ~Name.of.School)# Picking out a color palette
palette <- colorFactor(palette = "Set1",
domain = sch_merge$PolicyLevel)
# Changing our display from markers to circles, and adding the formatting information we want, about color, radius, outline, and opacity
sch_map_withFormats <- leaflet(sch_merge) %>% addProviderTiles("CartoDB.Positron") %>%
addCircles(lng = ~Longitude, lat = ~Latitude, popup = ~Name.of.School,
color = ~palette(PolicyLevel), radius = ~Totals/3, stroke = FALSE,
opacity = 0.8, fillOpacity = 0.8)
sch_map_withFormatsAdding a legend:
# Using the map that we just created, but adding a legend, giving it the information on colors, values, and title that we want.
sch_map_addedLegend <-
sch_map_withFormats %>%
addLegend(position = "bottomright", pal = palette, values = ~PolicyLevel,
title = "CPS School Policy Levels")
sch_map_addedLegendWe can create informative pop-ups using any of the information in out data sets, using basic HTML to create formats and web links.
detailedPopUp <- with(sch_merge, paste(sep = "<br/>",
paste0("<b>", Name.of.School, "</b> (<a href='", Website,"'>web</a>)"),
paste0(Street.Address, " ", City, ", ", State, " " , ZIP.Code),
paste0("Network: ", Network),
paste0("Policy Level: ", PolicyLevel)))
map_wPops <- leaflet(sch_merge) %>% addProviderTiles("CartoDB.Positron") %>%
addCircles(lng = ~Longitude, lat = ~Latitude, popup = ~detailedPopUp,
color = ~palette(PolicyLevel), radius = ~Totals/3, stroke = FALSE,
opacity = 0.8, fillOpacity = 0.8)
map_wPopsJust as we can add points, we can also work with polygons. Here, we use R to read a shape file and merge in ACS data that we will use to create data symbolization.
# First, we load the rgdal package--the R Geo-spatial Data Abstraction Library--
# which will allow us to do a variety of GIS data manipulations
library(rgdal)
# Next, we load in a shape file representing all of the Census tracts in Cook County which already has ACS data merged in, and transform it into local coordinate reference system.
# (Note: The "acs" package allows for downloading and working with ACS data directly in R. We don't use that here for the sake of space.)
chiTracts <- readOGR(dsn = "../data/CensusTracts_acsMerge.shp",
layer = "CensusTracts_acsMerge",
encoding = "UTF-8",
verbose = FALSE)
chiTracts <- spTransform(chiTracts, CRS("+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs"))
#Prepare colors for symbolizing our polygon, and add it to the map
quantpal_bl <- colorQuantile(palette = "Blues", domain = NULL, n = 5)
map_wPolys <- leaflet(sch_merge) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = chiTracts,
fillColor = ~quantpal_bl(chiTracts$rEd_LtHS),
color = "#BDBDC3", weight = 0.5, fillOpacity = 0.8) %>%
addCircles(lng = ~Longitude, lat = ~Latitude, popup = ~detailedPopUp,
color = ~palette(PolicyLevel), radius = ~Totals/3, stroke = FALSE,
opacity = 0.8, fillOpacity = 0.8)
map_wPolysquantpal_gr <- colorQuantile(palette = "Greens", domain = NULL, n = 5)
map_wLayers <- leaflet(sch_merge) %>%
addProviderTiles("CartoDB.Positron") %>%
addCircles(lng = ~Longitude, lat = ~Latitude, popup = ~detailedPopUp,
color = ~palette(PolicyLevel), radius = ~Totals/3, stroke = FALSE,
opacity = 0.8, fillOpacity = 0.8, group = "Schools") %>%
addPolygons(data = chiTracts,
fillColor = ~quantpal_bl(chiTracts$rEd_LtHS),
color = "#BDBDC3", weight = 0.5, fillOpacity = 0.8,
group = "% Ed < HS") %>%
addPolygons(data = chiTracts,
fillColor = ~quantpal_gr(chiTracts$rNonWhite),
color = "#BDBDC3", weight = 0.5, fillOpacity = 0.8,
group = "% Non-White") %>%
addLayersControl(
baseGroups = c("% Ed < HS", "% Non-White"),
overlayGroups = c("Schools"),
options = layersControlOptions(collapsed = FALSE))
map_wLayers